arXiv:2606.06991v1 Announce Type: cross Abstract: Online Video Large Language Models (Video-LLMs) have advanced toward seamless human-AI interaction through frame-by-frame processing and proactive responding. However, a critical challenge remains in streaming scenarios: existing models typically pause video perception while generating responses, breaking real-time video-language synchrony and causing stutters. To address this, we introduce a novel paradigm for online video understanding: Streaming Video-Language Synchrony (SVLS), and present LyraV, a live streaming assistant built upon a hiera
The rapid advancement of Video-LLMs and the increasing demand for real-time human-AI interaction in streaming applications are driving the need for continuous perception and response.
This development addresses a critical limitation in current online video understanding, moving towards seamless and synchronous multimodal AI interaction, which is essential for agentic systems.
Existing Video-LLMs will no longer need to pause video perception during response generation, enabling truly real-time, uninterrupted video-language synchrony.
- · AI developers
- · Streaming platforms
- · Real-time AI application providers
- · Consumers of AI services
- · Legacy online video understanding models
- · Applications reliant on asynchronous video-LLMs
Online video understanding becomes more fluid and responsive, enhancing user experience in live AI interactions.
This improved synchrony could accelerate the development and deployment of more sophisticated AI agents in diverse streaming and interactive environments.
The principle of continuous perception and action might extend beyond video, influencing the design of other real-time multimodal AI systems.
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Read at arXiv cs.AI